Enterprise Software-as-a-Service (SaaS) platforms increasingly rely on complex algorithmic systems that dynamically adjust user experiences, resource allocations, and operational parameters. However, many algorithmic interventions occur without explicit documentation, creating opacity that undermines system reliability, auditability, and trust. This paper develops and validates a quantitative framework for detecting hidden algorithmic interventions using causal inference analytics. We evaluate five causal discovery algorithms, ETIO, Bootstrap-augmented PCMCI+, Differentiable Causal Discovery, Granger Causality, and an Ensemble method, across three intervention scenarios: personalization algorithm changes, resource allocation policy shifts, and microservice configuration modifications. Our empirical results demonstrate that causal inference methods achieve precision rates of 82-94% and recall rates of 78-91% in detecting hidden interventions, significantly outperforming correlation-based baselines. Time-series causal methods excel in temporal scenarios, while ensemble approaches achieve optimal overall performance with F1-scores of 0.89-0.92. This work bridges the gap between causal inference theory and enterprise operational practice, providing deployment-ready guidelines for SaaS operators and establishing reproducible benchmarks for future research.

Causal inference, algorithmic interventions, SaaS platforms, causal discovery, enterprise analytics, root cause analysis

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